code: def load_llama_based(model_name, device="cuda"):
      tokenizer = LlamaTokenizer.from_pretrained(
          model_name, use_fast=False
      )
  
      model = LlamaForCausalLM.from_pretrained(
          model_name,
          load_in_8bit=False,
          device_map="auto",
          torch_dtype=torch.float16,
          low_cpu_mem_usage=True,
      )
      model_wrapped = HF_LM(model, tokenizer, device=device)
      return model_wrapped
  
file_path: interpret-text/python/interpret_text/generative/model_lib/models.py
imports: import torch
  from transformers import AutoTokenizer, AutoModelForCausalLM, T5Tokenizer, T5ForConditionalGeneration
  from transformers import LlamaTokenizer, LlamaForCausalLM
  from openai_tooling import CompletionsOpenAI, ChatOpenAI
  from hf_tooling import HF_LM
  from accelerate import Accelerator
label_desc: Use image node_id 3 for calling a global function with example usage: load_llama_based(model_name, device) and returns: model_wrapped